Malaria risk mapping among children under five in Togo

Malaria is a major health threat in sub-Sahara Africa, especially for children under five. However, there is considerable heterogeneity between areas in malaria risk reported, associated with environmental and climatic. We used data from Togo to explore spatial patterns of malaria incidence. Geospatial covariate datasets, including climatic and environmental variables from the 2017 Malaria Indicator Survey in Togo, were used for this study. The association between malaria incidence and ecological predictors was assessed using three regression techniques, namely the Ordinary Least Squares (OLS), spatial lag model (SLM), and spatial error model (SEM). A total of 171 clusters were included in the survey and provided data on environmental and climate variables. Spatial autocorrelation showed that the distribution of malaria incidence was not random and revealed significant spatial clustering. Mean temperature, precipitation, aridity and proximity to water bodies showed a significant and direct association with malaria incidence rate in the SLM model, which best fitted the data according to AIC. Five malaria incidence hotspots were identified. Malaria incidence is spatially clustered in Togo associated with climatic and environmental factors. The results can contribute to the development of specific malaria control plans taking geographical variation into consideration and targeting transmission hotspots.

in the surrounding area.In contrast, there are cold spots of malaria, defined as a location of cases where the intensity of transmission incidence is significantly lower than expected 15 .
Hotspots are the main reservoirs of persistent malaria transmission and are associated with higher vector density, sporozoite prevalence, and malaria incidence than in neighboring areas 16 .Malaria hotspots occur mostly in poor, tropical and subtropical areas of the world, with Africa being the most affected.The main risk factors of malaria hotspots are associated with local climatic and environmental conditions, such as proximity to vector breeding sites, precipitation 15 , vegetation cover, temperatures, housing conditions 17,18 , net use and household occupancy 19 .Nevertheless, the exact role of these factors in the formation of malaria hotspots is still under debate [20][21][22] .Several African countries have observed the persistence of malaria hotspots after an overall reduction in malaria transmission 21,22 .Lack of knowledge about malaria hotspots can therefore undermine the effectiveness of intervention programs.Identifying these hotspots is crucial to achieving the sustainable development goal of zero malaria incidence in a given area by 2030 23,24 particularly in Togo.
The aim of this study was to use geospatial techniques to identify malaria hotspots on the basis of environmental and climatic factors in a Togolese context, as availability of national data from the 2017 Malaria Indicators Survey provided a unique opportunity to assess spatial and temporal patterns of malaria transmission.

Study areas
Togo is a country located in West Africa, on the coast of the Gulf of Guinea.It is bordered to the north by Burkina Faso, to the south by the Atlantic Ocean, to the east by Benin and to the west by Ghana (Fig. 1).In 2022, Togo had a population about 8 million inhabitants with a density of 152 inhabitants/km2.It covers an area of 56,785 km 2 with 95.8% land and 4.2% water.It stretches over a length of 600 km and a width varying between 50 and 150 km.The highest altitude is at 986 m.

Study design and data source
Cross-sectional data were collected as part of the Togo Malaria Indicator Survey (MIS) 2017-2018, which is a national representative survey.The main objective of this MIS was to obtain population-based estimates for malaria indicators such as prevalence and risk factors.Standardized household cluster sampling methods were applied.Figure 1 presents the map of the 171 study clusters where data were collected.
The distribution of study clusters reflected a higher concentration in more densely populated areas as sampling was proportional to population size.The data collected included geospatial covariates such as temperature, aridity, rainfall, precipitation, proximity to water and vegetation derived from various sources, at different levels of national coverage 25 .
Malaria incidence data were calculated as the mean number of people per year with fever, in the 2 km (urban) or 10 km (rural) buffer zone surrounding the survey cluster location.Cases obtained from each country surveillance system are reported by the National Malaria Control Program (NMCP).This includes among others information on the number of suspected cases, number of tested cases, Number of positive cases by method of detection and by species as well as number of health facilities that report those cases.This information is summarized in a District Health Information Software (DHIS2) application 25,26 .The DHS experts made a link between DHS data with routine health data, health facility locations, local infrastructure such as roads and rivers, and environmental conditions.Aside from this, data on malaria incidence was obtained from all 171 MIS clusters in the country with a 5 year interval (2000, 2005, 2010 and 2015).The Table 1 below presents the descriptions of the different variables included in the study.

Statistical analysis
Analyses of malaria incidence and climatic and environmental factors was performed through the computation of mean difference, frequency counts, percentages, independent t-test, and one-factor analysis of variance (ANOVA).Geospatial modelling exploring malaria incidence in relation to environmental and climatic variables, including temperature, rainfall, precipitation, distance to water bodies, aridity and population density were performed using the Ordinary Least Squares (OLS), the Spatial Lag Model (SLM), and the Spatial Error Model (SEM).The non-spatial OLS model provides a global model of the variable or process one is trying to understand or predict 27 ; it describes the relationship between one or more quantitative variables and a dependent variable provides the covariate adjustment and prediction of mean risk of malaria in an area 28 .The spatial SLM model can accommodate a spatial dependency between the dependent variable and explanatory variables by incorporating a "spatially-lagged dependent variable" in the regression model.This model accounts for autocorrelation in the model with the weight matrix.The spatial SEM model is a model that considers corrective measures for residual spatial autocorrelation.For this model, the residuals were not assumed to be independent; instead, they exhibited a moving average map pattern 27,29 .The aim of using both spatial and non-spatial models is to compare the best-fitting models for predicting malaria incidence in children, using Akaike Information Criterion (AIC) to decide, and also to produce the most plausible risk maps 19,27,29 .The AIC allows us to select the model that minimizes the loss of information.We chose these models since we it to provide unbiased point estimates of the parameters.They offer the possibility to estimate spatial overlap coefficients, thus informing about correlation or spatial influence processes 30 .
Overall spatial autocorrelation between an ecological predictor and the incidence of malaria was assessed with Moran's I statistical test 27 .The Moran scatter plot was used to quickly obtain an overview of the global spatial autocorrelation of malaria incidences, and a Local Indicators of Spatial Association (LISA) analysis was used to identify hot and cold spots of cluster location 27,28 .The analyses were performed using R software.

Ethics approval and consent to participate
For this study, ethics approval was not sought since our analysis was based on publicly available data.However, DHS reports that informed consent, both written and verbal, was obtained from all participants by the Institutional Review Board of ICF International and the Bioethics Committee for Health Research (BCRS) of Togo.Prior to the start of the investigation, all ethical guidelines governing the use of human subjects were strictly adhered to and the methods were applied in accordance with the relevant guidelines and regulations of the Declaration of Helsinki.The data set and permission to conduct secondary data analysis were granted by the DHS program.

Results
Spatial and temporal distribution of the mean malaria incidence (per 1000 population at risk) The spatial distribution of the mean malaria incidence in Togo in 2000, 2005, 2010 and 2015 is presented in Fig. 2 below.Overall, between 2000 and 2015, the mean malaria incidence decreased, although an increase in the average malaria incidence was observed in the Savanes region until 2010.The Savanes and Central health regions had a higher average malaria incidence than the Plateaux and Kara regions in 2000.The evolution of the incidence of malaria in 2010 shows an aggravation in the Savannah and Kara regions.The Lomé commune region recorded the lowest average malaria incidence rates in 2000, 2005, 2010 and 2015.

Spatial autocorrelation analysis of malaria incidence
The effects of the most important environmental and climate factors as described in the methods are presented in Table 2.Both climatic and environmental factors, in particular temperature, aridity, precipitation and proximity to water, were related to the mean malaria incidence in each of the three analysis models.
The SLM model with the lowest AIC (− 65.906) was the best model with the best fit.For mean temperature, there was a positive significant association with malaria incidence in children under five.Each additional onedegree Celsius increase in mean temperature (26-29 °C) being associated with an increase in incidence of about 0.55%.For precipitation, with every millimeter of additional water precipitated, there was an associated increase in malaria incidence of the order of 0.392% in Togo.Malaria incidence also increased with an increase in aridity index and with proximity to water.In contrast, annual rainfall and population density might bean inversely associated with mean malaria incidence.www.nature.com/scientificreports/

Spatial autocorrelation: global moran's I
The global Moran's I test (Moran I = 0.469, p < 0.05) showed significant spatial dependence in the observed malaria incidences.Figure 3 depicts a scatter plot of mean malaria incidence.

Spatial autocorrelation: local indicators of spatial association (lisa)
The LISA results are shown in Fig. 4 below.The areas shown in red in Fig. 4a are the locations of clusters representing hot spots.The areas shown in blue are the locations of clusters representing cold spots.Figure 4b shows the significance levels, with different colors representing different p-value ranges.Dark green represents areas with significant (p-value less than 0.001) local spatial autocorrelation in malaria incidence; simple green represents areas with significant (p-value less than 0.01) local spatial autocorrelation in malaria incidence; and bright green represents areas with significant (p-value < 0.05) local spatial autocorrelation in malaria incidence.

Discussion
Malaria remains a serious threat in sub-Saharan Africa, including Togo, even if incidence has decreased overall, with significant differences in incidence between regions over the years.This study used spatial and non-spatial regression models to identify hotspots of malaria infection transmission in Togo through ecological analysis.Spatial heterogeneity could be explained by the precipitation seasonality, mean temperature, aridity index and proximity to water which were all positively correlated with malaria incidence in our analyses.All these factors are predominant environmental factors that characterized the Plateaux region where the highest incidence was observed and favour malaria transmission in the region 10 .For all the ecological predictors, some, as mentioned above (the mean temperature, aridity, precipitation seasonality and proximity to water), showed positive correlations and significant associations with malaria incidence.The daily survival rate of mosquitoes is optimal at about 90% with temperatures between 16°C and 36°C (Craig 1999).Increasing temperature reduces the blood meal-seeking behaviour of female Anopheles mosquitoes, resulting in a corresponding decrease in ovulation and juvenile mosquito production, and consequently a decrease in the number of new malaria cases.Increased precipitation was also positively associated with malaria incidence and can have both a direct and indirect effect, particularly where dams are located.Precipitation increases reservoir water levels and creates potential breeding grounds for mosquitoes along the banks of the reservoir 12,31 .The southern regions (Lomé commune, Maritime, Plateaux and Central) benefit from a longer duration of rainfall than the northern regions (Kara and Savanes) in Togo 10 .Concerning aridity, this very strong positive correlation with malaria incidence may be related to the fact that increased aridity influences malaria transmission by reducing the mosquito biting rate and adult life span as well as the extrinsic incubation period of the malaria parasite 18,31 .Thus, the ability of adult vectors to survive long enough and contribute to the spread of parasites, and the ability of pre-adult stages to sustain a minimum population, depends on aridity levels and species-specific resilience to arid conditions 32 .In contrast, a study in northeastern Benin 33 georeferencing all mosquito breeding sites in two rural sites showed that aridity had a negative influence on malaria transmission.According to the authors, this could be due to the fact that classic anopheles breeding sites have dried up due to the very high aridity in these regions, leading gravid females to lay their eggs in unusual habitats already hosting larvae of other general species.As for proximity to water bodies, our results corroborate with studies in Nigeria 19 and The Gambia 34 .Using respectively logistic regression methods and post-hoc analyses on mosquitoes catches, these studies observed a positive relationship with distance to water bodies up to 4 km before decreasing.
Other ecological predictors showed inverse but non-significant relationships, such as rainfall and population density.The inverse correlation with rainfall observed in the three analysis models suggests a negative impact of continuous heavy rainfall on malaria parasite vectors and eventual transmission of the disease.Similar previous studies conducted in Burkina Faso 10,31,32 also showed a negative correlation of rainfall on malaria incidence.Another study in Sri Lanka 35 observed seasonal variation in malaria infection in the country.The varying seasonal effects of rainfall on the number of malaria cases was reflected in the weak correlations observed, in which situation rainfall may be of limited use in predicting malaria.An observed linear relationship between rainfall and malaria could hide non-linear effects.An important aspect would be to investigate the relationship between rainfall and mosquito reproduction and survival.Variables influenced by rainfall, such as soil water saturation and river flow, could be more directly linked to the specific breeding conditions of malaria vectors.However, these variables are more costly to measure and are therefore often estimated from rainfall 17 .
Prevention measures are also likely to have a significant temporal effect on the time series of malaria cases.E.g. a study in urban and peri-urban areas in Africa (Mozambique and Senegal) and in the Indian Ocean (republic of Mauritius) 36 found a negative association between malaria infection and vector abundance.They attributed this to the willingness of people to use mosquito nets or to the stimulated development of immunity during early childhood in high-risk areas 37 .This variable has not been considered.Moreover, control methods and insecticides have changed over time, making it a complex variable 35 .
A limitation of spatial statistical models is that disease data can show a great deal of intra-and inter-annual variability, while a regression analysis assumes that the association between exposure and outcome is stationary over time 29 .Generalized linear mixed models (GLMM) could be used to capture transmission dynamics over time while controlling for temporal autocorrelations 38 .
Nevertheless, the data illustrate the opportunity to identify hotspots for malaria control.With regard to seasonal climate forecasts, environmental monitoring and the evolution of malaria morbidity in the country, these results can help design an early warning system for the National Malaria Control Program, which will help identify areas at risk of epidemic outbreak.

Conclusion
We evaluated the spatial and temporal association in Togo between malaria transmission hotspots and environmental and climatic risk factors.Malaria incidence was more related to incidence in nearby clusters than to those far away.Mean temperature, precipitation, aridity and proximity to water bodies showed a significant and positive association with malaria incidence.These results may help to channel available resources to disease hotspots and sustain prevention efforts for better control of malaria infection in children under 5 years of age in Togo and in similar sub-Saharan context.

Figure 4 .
Figure 4. (a) LISA map (b) and LISA significance map of Local Moran's I test show the hot and cold malaria spot locations in Togo using GeoDa software.

Table 1 .
Overview of data sources for the climatic and environmental variables.

Table 2 .
Spatial models showing correlation between the mean malaria incidence and climatic and environmental variables.Significant values are in bold.